Governance WPS 2010 12

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DEPARTMENT OF ECONOMICS AND FINANCE WORKING PAPER SERIES DECEMBER 2010

The Impact of Governance on Economic Growth: Further

Evidence for Africa

Bichaka Fayissa*Department of Economics & Finance, Middle Tennessee State University, TN

Christian NsiahBlack Hills State University, Spearfish, SD

Abstract

Sub-Sahara African countries have had a checkered past when it comes to good governance and good institutions.Increasingly, economists and policy makers are recognizing the importance of good governance and institutions foreconomic growth and development. The New Partnership for Africas Development (NEPAD) which was initiatedby the African Heads of State and endorsed by the G8 countries including the European Union, Japan, and China inOctober 2001 has four main goals: eradicating poverty, promoting sustainable growth and development, integratingAfrica into the world economy, and accelerating the empowerment of women. The NEPAD objectives are based onthe underlying principles of a commitment to good governance, democracy, human rights and conflict resolution,and the recognition that the maintenance of these standards is fundamental to the creation of an environmentconducive to investment and long-term economic growth. The objective of this paper is to investigate the role ofgovernance in explaining the sub-optimal economic growth performance of African economies while controlling forthe conventional sources of growth. Our results suggest that good governance or lack thereof contributes to the gapsin income per capita between richer and poorer African countries. Furthermore, our results indicate that the role ofgovernance on economic growth depends on the type and the level of income growth of countries underconsideration.

Key Words: Workers Remittances, Economic Growth, Panel Data, Arellano-Bond, Quantile

Regression, Sub-Saharan Africa

JEL Classification: E21 F21, G22, J61, O16

* Corresponding author: Bichaka Fayissa, Professor of Economics, Department of Economics & Finance, MiddleTennessee State University, Murfreesboro, TN 37132, Tel. (615) 898-2385, Fax (615) 898-5596, Email:[email protected]


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The Impact of Governance on Economic Growth: Further Evidence for Africa

I. Introduction

The growth literature is replete with empirical studies which have considered the impact of the

conventional sources of growth including investment in physical and human capital, labor, trade, aid,

foreign direct investment (FDI), geography, and a variety of other variables within the neoclassical

growth framework. Since the end of the Cold War until the early 1990s, however, the issue of good

governance has become an important concept in the international development debates and policy

discourse.

The working definition of what constitutes good governance has evolved over the years. Schneider

(1999) defines good governance as the exercise of authority, or control to manage a countrys affairs and

resources. The United States Agency for International Development (USAID, 2002), on the other hand,

defines good governance as a complex system of interaction among structures, traditions, functions, and

processes characterized by values of accountability, transparency, and participation. The UNDP (2002)

defines good governance as striving for rule of law, transparency, equity, effectiveness /efficiency,

accountability, and strategic vision in the exercise of political, economic, and administrative authority.

Historically, sub-Saharan African countries have had a checkered good governance record in comparison

to other regions of the world. These countries have been bogged down with political instability,

government ineffectiveness, the lack of rule of law, and serious problems of corruption which are signs of

bad governance. With respect to the importance of good governance to development, improving

governance in this region has been given a central place in the New Partnership for Africas Development

(NEPAD). Over the past few years, some countries in this region including, but not limited to Botswana

and Ghana, have made significant progress in terms of governance.


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Recently good governance has become conditionality for the disbursement of development assistance to

less developed nations. Furthermore, foreign investors are increasingly basing their investment decisions

on good governance. Granted, there are some economists including Owens (1987), and Sen (1990) who

recognized and advocated for the need for political and economic freedom as an essential dimension for

economic growth, these studies were theoretical discourses rather than being empirical expositions. Since

1990s, however, empirical studies in this area have dealt with the effects of lack of good governance

rather than its direct impact on the economic growth of emerging countries.

Given that the governance situation differs from one sub-Saharan African country to the other, the

objectives of this inquiry are twofold. First, we investigate the effect of various governance indices on

economic growth of sub-Saharan African countries while considering the conventional sources of growth.

Second, we investigate whether the impact of these governance indicators differ by the conditional

distribution of economic growth. Thus, we investigate whether the impact of governance on economic

growth depends on the relative level of growth.

The rest of the paper is organized as follows. Section II provides a review of selected literature. In section

III, we specify a conventional neoclassical growth model which incorporates remittances as one of the

sources of growth and we also specify a quantile regression model. Section IV presents estimation results

for both the fixed and random effects regressions accounting for both the country and time effects and

quantile regression estimation. The last section summarizes the results, draws conclusions, and makes

some policy recommendations for promoting remittances as a growth and development strategy.

II. A Review of Selected Literature

Earlier studies including Owens (1987) and Sen (1990) have argued for the need for economic and

political freedom as necessary conditions for the economic growth and development of nations.


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Nevertheless, most of the previous studies only considered certain dimensions of governance which are

theoretical in nature. Empirical studies that have been undertaken since 1990s primarily dealt with the

effects of poor governance (as proxied by political and export instabilities and corruption) on the sources

of growth rather than its direct impact on growth.

Keefer et al. (1997) find that institutions such as property rights and contract enforcement positively

influence economic growth. Campos and Nugent (1999) also find that the institutions of governance

improve the development performance. Kaufmann, et al. (1999a and 1999b) identify the problems

associated with the aggregation of good governance measures, but conclude that good governance matters

for development.

In a cross-sectional analysis of all developing countries, Chauvet and Collier (2004) found that those

countries suffering from poor governance, on average, experience 2.3 percentage points less GDP growth

per year relative to other developing countries. There are also other recent findings that suggest a strong

causal effect running from better governance to better development outcomes.1

In spite of such a broad array of support for the positive impact of good governance on economic growth,

there are only few studies that show results to the contrary. For example, an important challenge to the

significance of good governance for the economic growth of African countries comes from Sachs et al.

(2004). In an empirical analysis, they show that the differences in performance among African countries

cannot be explained by differences in the quality of their governance once differences in their levels of

development have been accounted for and thus conclude that a focus on governance reforms is

misguided.2

1 See Knack and E.Keefer (1995), Mauro (1995), and Acemoglu, et al. (2004)

2Doornbos (2003) acknowledges that the metamorphosis of good governance is a policy metaphor.


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The above findings which appear to contradict each other signify the need for more research in this arena.

Our study seeks to reconcile the two opposing research findings by first focusing our analysis only on

African countries, secondly investigating the impact of the different measures of good governance (such

as voice and accountability, political stability, government effectiveness, regulatory quality, rule

of law, and control of corruption) while controlling for the conventional sources of growth, and

also examine the impact of the composite index of good governance on the economic growth of Sub-

Sahara African countries. Furthermore, we use quantile regression analysis to investigate if the impact

of governance on economic growth differs by the composition of the income distribution of African

countries (see the appendix for a brief exposition of quantile regression) . In a recent study of institutions,

governance, and economic development in Africa, Fosu, et al. (2006) draw the conclusion that while

politically accountable governments can lead to improved economic outcomes, they are unlikely to adopt

economically desirable policies that are unpopular with the populace and that the tendency of such

governments which increases the risk of political discord may actually stand in the way of a meaningful

economic growth path. Our study may also shed some light on the validity of the above observations for

making growth-enhancing governance policy recommendations. We now turn to the specification of an

empirical model for the analysis of the impact of the various governance measures described above while

controlling for the conventional sources of growth in section III.

III. An Empirical Model of Economic Growth with Governance

This study employs panel data for 28 sub-Saharan African countries for the years between 1990 and 2004.

The choice of countries and time series data rests on the availability of data. Except for the governance

indicators (which are taken from the Fraser Institutes Economic Freedom of the World Index) and the

foreign financial flow data (which are taken from the UNCTAD Handbook of Statistics) all data are from

the World Bank Development Indicators (WDI, 2006) CD. The definitions and descriptive statistics of

each variable included in the growth model are provided in Tables 1 and 2, respectively.


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Our primary goal is to investigate whether good governance has an impact on the economic

growth of African countries and thus may explain the differences in their economic growth. Furthermore,

we seek to determine whether the impact of good governance is similar along all conditional distributions

of income (i.e. low, middle, and high groups). While the focus of this study is on the impact of good

governance on economic growth, we also take into consideration the traditional sources of economic

growth such as investment in physical and human capital, openness to trade, foreign investment, and

official development assistance. We specify a simple double log-linear Cobb-Douglass production

function as:

(1)

where denotes the estimated coefficients, i and t denote the ith country and the tth time period,

respectively. PCIit is the natural log of real GDP per capita; GCF is the log of gross fixed capital

formation which is used as a proxy for investment in physical capital; SCH is secondary and tertiary

school enrollment as a percentage of the gross enrollment used as measure of investment in human

capital; TOTis the log of trade as a percent of GDP for each country under consideration to capture the

impact of openness of the economy on economic growth; AID denotes official development assistance

and foreign aid in current US$; FDIis the log of foreign direct investment flows in US$ as a percent of

real GDP;HHCdenotes real household consumption expenditure per capita, whereas as OIL is the log of

crude oil production; DPRit is the log of dependency ratio; TEL denotes the log of landline phones per

thousand population; Last, GGk denotes each of the six indicators of good governance and the composite

index of good governance).

We hypothesize a positive relation between investment in physical capital (GCFit), investment in human

capital (SCHit), the openness of the economy (TOTit), and real GDP per capita income (PCIit). Intuitively,


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it makes sense to expect that foreign direct investment (FDI) will promote growth in the host country, not

just by providing direct capital financing, but also creating positive externalities via the adoption of

foreign technology and know-how. The empirical literature, however, finds mixed evidence on the

impact of foreign direct investment on host countrys economic growth. The conclusions made by related

literature range from significantly positive (Ram and Zhang, 2002 and Campos and Kinoshita, 2002) to

insignificant (Carkovic, and Levine 2002), and to significantly negative (Dutt 1997 and Saltz 1992).

Other macro level studies also suggest that country characteristics are important in determining the

contributions of FDI to growth. For example, Borensztein et al. (1998) and Xu (2000) point out that FDI

leads to positive growth only if certain minimum stock of human capital exist in the host country,

whereas Alfaro et al. (2002) and Durham (2004) argue that only countries with well developed financial

markets realize significant growth rates due to FDI. Since the effect of foreign direct investment (FDI it)

on economic growth has been mixed, the expected relation may be ambiguous (i.e., positive or negative).

The impact of foreign aid(AIDit) on economic growth is also controversial. In her recent book titled Dead

Aid, Moyo (2009) argues that aid disbursements which are especially in the form of concessional loans

and grants have hampered, stifled, and retarded Africas development. Some studies including Hansen

and Tarp (2000; 2001) and Dalgaard et al. (2004) find a positive impact while others including Mosley

(1980) and Shan (1994) identify a negative impact of aid on growth. On the other hand, Fayissa and El-

Kaissy (1999) and Burnside and Dollar (2000) conclude that aid has a positive impact on growth in

developing countries with good policies and little effect in countries with poor policies. Using an

expanded version of the dataset of the latter study, Easterly et al. (2004) raise new doubts about the

effectiveness of aid even in the case of good policies. Thus, the effect of aid (AIDit) on economic growth

cannot be predicted a priori.

High dependency ratio (DPR) has been associated with low economic growth in the literature. The

argument put forth is that high DEPR dilutes the contribution of per worker real GDP growth to real per

capita GDP growth. Bloom and Sachs (1998) conclude that it negatively impacts national savings and


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human capital formation. Krugman (1994) stresses the importance of changes in DPR as the main driving

force for the rapid growth of several Asian economies in recent years.

Most previous literature shows a positive relationship between infrastructure and economic performance.

Datta and Agarwal (2004) indicate that telecommunications infrastructure played a positive and

significant role in economic growth in 22 OECD countries from 1980-1992. OECD (1994), and Roller

and Waverman (2001) examine the effects of telecommunication infrastructure investment and economic

performance and find telecommunication investment has a significant growth effect, particularly when

there is already a substantial network infrastructure in place. Easterly and Levine (1997) also find that

infrastructure development as measured by telephones per worker contributes to economic growth. We

follow the example of Easterly and Levine (1997) and proxy infrastructure investment with telephone

mainlines per thousand population(TEL). The relationship between infrastructure investment and

economic growth is expected to be positive.

We estimate the parameters corresponding to the explanatory variables of Eq. 1 above by the fixed-effects

and random-effects models based on panel data for 28 African countries spanning from 1990 to 2005. An

empirical representation of the model is provided in equation (2) below.

( )it i t it it Y X= + + + (2)

itY is the natural logarithm of real GDP per capita in country i at year t; and Xit is a vector of the

explanatory variables (investment in physical and human capital, terms of trade, foreign aid, foreign

direct investment, household consumption, oil production, dependency ratio, telecommunications

investment, and the measures of good governance) for country i = 1, 2, n and at time t= 1, 2, ,T; is

a scalar vector of parameters of1. 7; it is a classical stochastic disturbance term with E[it ]= 0 and

var [it ]= ,2, i and t are country and time specific effects, respectively. Instead ofa priori decision

on the behavior ofi + t, different types of assumptions are separately imposed on the model with the

one having robust estimates chosen.


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Assuming the country specific effects to be constant across countries and the time specific effects are not

present [i.e. i = and t =0)], model (2) then is being estimated by the Ordinary Least Squares (OLS)

method, or restricted OLS method. The second estimation technique assumes that the country specific

effects are constant, but not equal (i.e. i = i and t =0) which yields a One-Way fixed-effects model.

The third assumption presumes a situation where the country effects are not constants, but rather

disturbances; the time effects then are not present [i.e. i = + wi and t =0], where E [w i]=0 and

var[wi]= w2 and cov[i, wi] =0. In this case, model (2) is estimated by the Generalized Least Squares

(GLS) which yields a random-effects model.

Next, using a modified version of equation (2), we employ a quantile regression analysis to investigate

whether the impact of good governance on economic growth depends on the conditional economic

income distribution of countries. A quantile regression is a statistical technique intended to estimate and

conduct inference about conditional distribution functions. Just as the classical linear regression methods

based on the minimization of sums of squared residuals enables one to estimate models for conditional

mean functions, quantile regression methods offer a mechanism for estimating models for the conditional

median function and the full range of other conditional quantile functions. The estimation of conditional

mean functions with techniques for estimating an entire family of conditional quantile functions, allow us

to provide a more complete statistical analysis of the stochastic relationships among random variables

(Koenker and Billias, 2001).

The quantile regression model, first introduced by Koenker and Bassett (1978), and applied by Buchinsky

(1998) can be written as:

(3)


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where denotes the vector of log of gross domestic product per capita, xit is a vector of all the

independent variables used in the OLS type regressions , is a vector of the parameters to be estimated,

and uit is a vector of residuals. represents the conditional quantile of given .

The regression quantile (0 < < 1), solves the following minimization of the sum of absolute

deviations residuals:

(4)

Where is called the check function which is defined as:

(5)

By allowing to continuously change from zero to one, we are able to trace the impact of each

governance indicator and other control variables on the entire distribution of per capita GDP at any given

quantile. Thus, the unique feature of this methodology is that it allows us to relax the assumption made in

least squares regression where the parameter estimates are assumed to be the same at all points on the

conditional GDP distribution.

Thus, unlike the OLS estimator which provides the impact of an explanatory variable at the conditional

mean of the dependent variable, the quantilie regression derives estimates for different conditional

quantiles of the dependent variable. The coefficients can be interpreted as the partial derivative of the

conditional quantile of dependent variable with respect to particular explanatory variable. This derivative

can be interpreted as the marginal change in the dependent variable at the conditional quantile due to

the marginal change in a particular explanatory variable. In implementing the quantile regression to panel

data, Koenker (2004) suggests that unobserved firm level fixed-effects can be controlled by including


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firm dummies in the regression. We follow Koenker (2004) by incorporating country level dummies to

control for unobserved country level fixed-effects.

Following Koenker and Hallock (2001), this study fits a regression model for nineteen quantiles of per

capita income; they are evenly spaced at intervals of .5, starting at the first quantile and ending at the 9.5th

quantile. We use these regressions to check whether the impact of good governance on economic growth

varies by quantiles of conditional gross domestic product. The result of this analysis is presented in

Figures 1, panels A through G.

IV. Empirical Results and Interpretations

Several versions of equation 2 are tested in order to obtain a model which yields robust results and best

fits of the data. Accordingly, Table 2 presents the estimation results of the fixed-effects model whereas

Table 3 presents the estimation results for the random-effects model. Apart from the magnitude of the

coefficients, the results reported in Tables 2 and 3 are comparable.

A comparison of the consistent fixed-effects with the efficient random-effects estimates using the

Hausman specification test, rejects the random-effects estimates at p


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The results from our model of choice indicate that all the governance variables have positive and

statistically significant effects on the GDP per capita (atp < .05) of African countries. However, we find

that the magnitude and significance of the impact of good governance depends on the proxy of good

governance used. Accordingly, when the voice and accountability index (VAI) is used as the proxy for

good governance, a 10 percent improvement in the voice and accountability of a countys citizenry leads

to a .68 percent increase in its real per capita income. In the case of political stability (PSI), we find that a

10 percent increase in the political stability index of a country corresponds to a .37 percent rise in its real

per capita income. We find that a 10 percent improvement in a countrys government effectiveness index

(GEI) and regulatory quality (RQI) lead to a .73 and .61 percent increase in its real per capita income,

respectively. Similarly, we find that a 10 percent improvement in rule of law (RLI) and control of

corruption index (CCI) translate into a .21 and .15 percent rise in per capita income. When considering

the composite governance indicator (GOI) which is the unweighted average of all the six sub-categories

of good governance, we find that a 10 percent improvement in good governance, results in a .91 percent

increase in the real per capita income of a country.

For the quantile regression, although the analyses were done with all the explanatory variables used in the

fixed and random-effects models, we concentrate our discussion on the governance indicators, our

variables of interest in the interest of space. The results are presented in Figures 1 panels A through G.

In the case of the voice and accountability index (VAI), the results as presented in Panel A indicate that

voice accountability have a positive impact on all quantiles of income, except the 95 th quantile, but it has

a larger positive impact for the lower quantiles. Further, the graph indicates that several estimated

coefficients for the quantile regression fall outside the confidence interval area for the OLS estimates as

denoted by the dotted lines. This finding implies that the impact of voice and accountability on income

for those quantiles is significantly different from the OLS estimates.


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For the political stability (PSI), the results as presented in Panel B suggest that political stability also

hasa positive impact at almost all levels of growth. The graphical of the quantile regression estimates

indicate that political stability is much more important for low-income economies than for and high-

income economies.The graph also shows that several estimated coefficients for the quantile regression

fall inside the 95% confidence interval area for the OLS estimates as denoted by the dotted lines. This

finding indicates that the impact of political stability on income estimated for the conditional quantiles are

not significantly different from the OLS estimates.

In the case ofgovernment effectiveness, the quantile regression estimates as presented in Panel C indicate

that government effectiveness has a positive impact on growth at all economic level. The V-shape of the

graph though suggests that the impact of government effectiveness is more pronounced at lower and

upper levels of growth than for middle quantiles of economic growth. Further, apart from the estimate for

the 5th quantile, the graph indicates that the impact of government effectiveness on growth for all the other

quantiles falls within the 95% confidence interval area for the OLS estimate as denoted by the dotted

lines, suggesting that. the impact of government effectiveness on income estimates for the conditional

quantiles are not significantly different from the OLS estimates.

The quantile regression estimates for the regulatory quality in Panel C indicates that it has a positive

impact on economic growth at all levels of growth. However, the magnitude of the impact is higher at

very low levels of growth than for higher levels of growth. Further, the graph shows that several quantile

estimates fall outside the 95% confidence interval area for the OLS estimates, indicating that these

quantile estimates are significantly different from estimates derived from OLS type regressions.

The impact of rule of law on economic growth (Panel E) indicates that it has a positive impact on

economic growth at all levels of income. However, the graph shows that the impact of rule of law is

generally larger for lower levels of economic growth than at the higher levels of economic growth. The

graph shows that quantile estimates up to the 1st quantile fall outside the 95% confidence interval area for


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the OLS estimate (denoted by the dotted lines), indicating that those quantile estimates are significantly

different from the OLS estimates.

In the case ofcontrol of corruption, the quantile regression estimates show a positive impact of corruption

control on economic growth at all levels of growth (Panel F). The graph also indicates a small variation

between quantiles in terms of the magnitude of the impact of corruption control on economic growth.

Despite the visible difference between the quantile and OLS regression estimates of the impact of

corruption control on economic growth, most of the quantile regression estimates fall within the 95%

confidence interval area for the OLS estimate as denoted by the dotted lines, implying that the quantile

regression estimates are not significantly different from the OLS estimate.

Finally, the quantile regression estimation results for the overall measure of good governance indicator

(Panel G ) show that good governance has a positive impact at all levels of growth. The u-shape of the

graph indicates that good governance is desirable at all levels of growth, but it is more important for the

lower and upper quantiles than for the middle quantiles of economic growth.

V. Conclusion

The purpose of this inquiry has been to identify the impact of good governance on per capita income

growth for countries of the Sub-Saharan African region and to investigate whether the impact differs by

conditional distribution of GDP per capita. Six different sub-categories of good governance (voice and

accountability,political stability, government effectiveness, regulatory quality, rule of law, and control of

corruption) and one overall measure of good governance are analyzed. The empirical results are based

on annual panel of data of 28 African countries covering the years between 1995 and 2005. The results of

the alternative estimated models suggest that good governance has a positive and significant impact on

growth, regardless of the proxy used for good governance. Furthermore, the results indicate that the


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impact of good governance differs by the conditional distribution of the GDP per capita under

consideration.

The salient conclusions drawn from this study suggest that good governance is important for the

economic growth of sub-Saharan African economies, especially in those countries which are at the low

end of the income distribution spectrum. To reverse the persistent anemic economic growth trend in Sub-

Sahara Africa, both domestic and external policy makers may have to place significant emphases on the

maintenance of the voice and accountability,political stability, government effectiveness,regulatory

quality, rule of law, and control of corruption.

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Shan, J. (1994). Impact of Foreign Capital on Domestic Savings and Growth in Developing Economies,

Discussion Paper No. 13, School of Economics and Public Policy, Queensland University ofTechnology, April.

Schneider, H. (1999), Participatory Governance: The Missing Link for Poverty Reduction, Policy

Brief No. 17, Paris: OECD Development Centre.

Sen A., 1999, Development as Freedom, Alfred Knopf Publisher (New York: NY).

UNDP, 2002, Giving Voice to the Voiceless: Good Governance

USAID, 2002, USAID Supports Good Governance, http://www.docstoc.com/docs/673298/USAID-Supports-Good-Governance

Xu, B. (2000). Multinational Enterprises, Technology Diffusion, and Host Country Productivity Growth,Journal of Development Economics, 62, 477-493.


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Table 1: Variable Description and Summary Statistics

Variable Description Mean Std. Dev. Min Max

PCI GDP per capita (constant 2000 US$) 853.020 1326.883 56.520 7618.543SCH School enrollment, secondary + tertiary (% gross) 31.561 22.920 5.503 113.104

OIL Crude Oil including Lease Condensate Production 82.352 329.560 0.000 2328.962

AID Aid (% GNI) 14.260 17.595 0.000 210.561

FDI Foreign direct investment, net inflows (% of GDP) 45.403 150.415 -6.890 2001.110

GFC Gross fixed capital formation 20.351 11.265 1.802 113.578

HHC Household final consumption expenditure per capita (constant 2000 US$) 582.123 828.622 64.199 4955.969

TRD Trade as a percent of GDP 78.767 45.265 12.797 275.232

DPR Age dependency ratio (dependents to working-age population) 0.900 0.110 0.460 1.130

TEL Telephone mainlines (per 1,000 people) 24.879 51.059 0.180 286.660

VAI Voice and Accountability Index (0-100) 31.035 20.946 1.000 79.800

PSI Political Stability Index (0-100) 33.558 24.117 0.000 87.000

GEI Government Effectiveness Index (0-100) 28.808 21.649 0.000 78.200

RQI Regulatory Quality Index (0-100) 29.085 18.837 0.000 77.100

RLI Rule of Law Index (0-100) 28.934 21.503 0.000 81.000

CCI Control of Corruption Index (0-100) 29.137 19.693 0.000 84.000

GOV ((vap+psp+gep+rqp+rlp+ccp)/6) 30.093 18.103 0.567 76.067


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Table 2: Random Effects Estimation Results

Variables Coef. Coef. Coef. Coef. Coef. Coef. Coef.Constant 2.433 *** 2.433 *** 2.465 *** 2.514 *** 2.325 *** 2.166 *** 2.449 ***

(0.237) (0.240) (0.225) (0.229) (0.234) (0.232) (0.229)

SCH 0.060 *** 0.061 *** 0.058 *** 0.037 * 0.057 *** 0.049 ** 0.051 **

(0.021) (0.021) (0.020) (0.021) (0.021) (0.022) (0.021)

OIL 0.014 *** 0.014 *** 0.013 *** 0.012 *** 0.014 *** 0.014 *** 0.014 ***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

AID -0.036 *** -0.031 *** -0.040 *** -0.040 *** -0.028 ** -0.032 *** -0.039 ***

(0.012) (0.011) (0.011) (0.011) (0.011) (0.012) (0.011)

FDI 0.047 *** 0.049 *** 0.036 *** 0.040 *** 0.048 *** 0.053 *** 0.042 ***

(0.009) (0.009) (0.009) (0.008) (0.009) (0.009) (0.00()

GFC 0.017 0.021 0.020 0.012 0.024 0.042 ** 0.012

(0.020) (0.021) (0.018) (0.019) (0.020) (0.023) (0.019)HHC 0.485 *** 0.501 **** 0.467 *** 0.484 *** 0.523 *** 0.539 *** 0.471 ***

(0.041) (0.039) (0.037) (0.037) (0.037) (0.037) (0.038)

TRD 0.084 *** 0.073 *** 0.075 *** 0.071 *** 0.070 ** 0.084 *** 0.073 ***

(0.027) (0.028) (0.025) (0.071) (0.028) (0.027) (0.026)

DPR -0.289 * -0.289 * -0.228 -0.230 -0.305 ** -0.231 -0.254 **

(0.148) (0.149) (0.142) (0.143) (0.149) (0.154) (0.144)

TEL 0.088 *** 0.086 *** 0.088 *** 0.093 *** 0.083 *** 0.104 *** 0.094 ***

(0.022) (0.022) (0.021) (0.021) (0.022) (0.024) (0.021)

VAI 0.047 **

(0.018)

PSI 0.026 **

(0.012)GEI 0.071 ***

(0.013)

RQI 0.057 ***

(0.012)

RLI 0.021 *

(0.013)

CCI 0.012

(0.009)

GOV 0.084 ***

(0.019)

R-squared 0.8476 0.8361 0.862 0.855 0.837 0.827 0.863# of observ 384 384 384 384 384 384 384

Notes: Coeff. denotes estimated coefficients, and the number in parenthesis represents standard errors, ***,**, *, denotes significance at

the 1%, 5%, and 10% levels. The estimation includes 4 regional dummies and nine year dummies.


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Table 3: Fixed Effect Estimation Results

Variables Coef. Coef. Coef. Coef. Coef. Coef. Coef.

Constant 4.115 *** 4.106 *** 3.963 *** 4.027 *** 3.887 *** 3.897 *** 4.002 ***(0.250) (0.253) (0.232) (0.236) (0.250) (0.254) (0.237)

SCH 0.066 *** 0.068 *** 0.061 *** 0.039 ** 0.062 *** 0.062 *** 0.053 ***

(0.018) (0.019) (0.017) (0.018) (0.019) (0.019) (0.018)

OIL 0.005 * 0.006 ** 0.005 * 0.004 0.006 ** 0.006 ** 0.005 *

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

AID -0.023 ** -0.015 -0.025 *** -0.025 *** -0.012 -0.011 -0.024 **

(0.010) (0.010) (0.009) (0.010) (0.010) (0.010) (0.010)

FDI 0.028 *** 0.031 *** 0.018 ** 0.023 *** 0.031 *** 0.031 *** 0.024 ***

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

GFC 0.027 0.035 * 0.034 ** 0.025 0.040 ** 0.040 ** 0.025

(0.017) (0.018) (0.016) (0.016) (0.017) (0.019) (0.016)

HHC 0.207 *** 0.233 *** 0.228 *** 0.245 *** 0.276 *** 0.273 *** 0.221 ***

(0.043) (0.042) (0.038) (0.038) (0.040) (0.041) (0.040)TRD 0.043 * 0.027 0.036 0.031 0.030 0.040 * 0.033

(0.023) (0.024) (0.022) (0.022) (0.025) (0.024) (0.023)

DPR -0.315 ** -0.315 *** -0.239 ** -0.238 * -0.332 ** -0.325 ** -0.268 **

(0.130) (0.131) (0.125) (0.127) (0.134) (0.138) (0.127)

TEL 0.064 *** 0.059 *** 0.067 *** 0.071 *** 0.057 *** 0.054 ** 0.073 ***

(0.020) (0.020) (0.019) (0.019) (0.020) (0.023) (0.019)

VAI 0.068 ***

(0.016)

PSI 0.037 ***

(0.011)

GEI 0.073 ***

(0.011)

RQI 0.061 ***

(0.010)

RLI 0.021 **

(0.011)

CCI 0.015 **

(0.008)

GOV 0.091 ***

(0.016)

R-squared 0.8496 0.8441 0.8655 0.8686 0.8444 0.8328 0.8642

# of observ 384 384 384 384 384 384 384

Notes: Coeff. denotes estimated coefficients, and the number in parenthesis represents standard errors, ***,**, *, denotes significance at the 1%, 5%, and 10% levels.

The estimation includes 4 regional dummies and nine year dummies.


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Figure 1: Quantile Regression Analysis of the Governance Measures

Panel A: Voice Accountability

Notes: The solid line denotes the quantile regression estimates. The grey area denotes the bootstrap confidence interval for the

quantile regression estimate. The dotted lines denote the 95% confidence interval for the OLS estimate whereas the dash line

denotes the OLS estimate.

Panel B: Political Stability


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Panel C: Government Effectiveness




Panel D: Regulatory Quality

Notes:


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The solid line denotes the quantile regression estimates. The grey area denotes the bootstrap confidence interval for the quantile

regression estimate. The dotted lines denote the 95% confidence interval for the OLS estimate whereas the dash line denotes the

OLS estimate.

Panel E: Rule of Law

No




Panel F: Control of Corruption


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Panel G: Overall Good Governance

Notes: The solid line denotes the quantile regression estimates. The grey area denotes the bootstrap confidence interval for thequantile regression estimate. The dotted lines denote the 95% confidence interval for the OLS estimate whereas the dash line


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